<p>Gene expression profiling for predicting clinical outcome of breast
  cancer (van't Veer et al., 2002).  Microarray breast cancer data set
  of 4707 expression values on 78 patients with survival information.</p>

datasets

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Udaya Kogalur

vdv: van de Vijver Microarray Breast Cancer

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